A discussion on the uses of smart sensory network, cloud-computing, digital twin and artificial intelligence for the monitoring of long-span bridges

Abstract

[EN] This paper discusses what are the smart sensors that are currently available to be used for real-time monitoring of long-span bridges, how to develop an effective and efficient data strategy for collecting, processing, managing, analysing and visualising data sets from monitored assets to support decision-making, and how to establish a cost-effective and smart sensory network according to the objectives set up through thorough communications with asset owners. Due to high data rates employed and dense monitoring sensors installed the traditional processing technique will not fulfil the monitoring functionalities and is not secure. Cloud-computing technique is widely used in processing and storing big monitoring data sets. Using the experience attained by the authors in the establishment of long bridge monitoring systems in the UK and China this paper will compare the pros and cons of using cloud-computing for long-span bridge monitoring. It will further explore how to use digital twin (DT) and artificial intelligence (AI) for the extraction of relevant information or patterns regarding the health conditions of the assets and visualise this information through the interaction between physical and virtual worlds to realise timely and informed decision-making in managing essential road transport infrastructure.